File: C:/Program Files/R/R-4.4.0/library/translations/en@quot/LC_MESSAGES/R-stats.mo
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� � � 4 ? z � � � Y p � � � � - � � � � ! � a I � � � � u � � J � � � Y % � X � $ � \ � � � C O � � p � \ � � c � � � � � � * , ? � %d factor is too many for %d variables %d factors are too many for %d variables %d missing value deleted %d missing values deleted %d observation deleted due to missingness %d observations deleted due to missingness %d observation with NA, NaN or Inf deleted %d observations with NAs, NaNs and/or Infs deleted %s applies only to two-way tables %s link not recognised %s must be 0 or 1 %s needs package 'Matrix' correctly installed %s: NAs produced for non-estimable cases %s; attr(*, "non-estim") has doubtful cases %s; consider predict(., rankdeficient="NA") '%s' cannot be of mode '%s' '%s' must be a character string '%s' must be numeric in [0, 1] 'A' must be an array or table 'ColSideColors' must be a character vector of length ncol(x) 'P.values' is TRUE, but 'has.Pvalue' is not 'RowSideColors' must be a character vector of length nrow(x) 'T' must be nonnegative 'V' is not a square numeric matrix 'X' is not a dendrogram 'X' matrix has %d case (row) 'X' matrix has %d cases (rows) 'X' matrix was collinear 'Y' has %d case (row) 'Y' has %d cases (rows) 'acf' must be of length two or more 'all.knots' is TRUE; 'nknots' specification is disregarded 'all.knots' is vector of knots; 'nknots' specification is disregarded 'alpha', 'beta' and 'gamma' must be within the unit interval 'anova' is only defined for sequences of "nls" objects 'anova' object must have colnames 'approx' requires n >= 1 'ar' part of model is not stationary 'bw' is not positive. 'by' must be a list 'centers' must be a number or a matrix 'circular' must be logical and not NA 'coef' does not have the correct length 'coef' must be a vector 'coef' must define a contrast, i.e., sum to 0 'coef' must have same length as 'contrast.obj' 'col.vars' missing or incorrect 'conf.level' is not a probability 'conf.level' must be a single number between 0 and 1 'contrasts.arg' argument must be named 'control' argument must be a named list 'covmat' is not a valid covariance list 'covmat' is of unknown type 'cterms' argument must match terms in model object 'cutpoints' must be unique in 0 < cuts < 1, but are = 'cutpoints' must be unique, but are = 'cv' must not be NA when 'df' is specified 'data' must be a data.frame, environment, or list 'data' must be a data.frame, not a matrix or an array 'data' must be a list or an environment 'degree' must be 0, 1 or 2 'degree' must be at least 1 'degree' must be less than number of unique points 'deriv' must be between 0 and 3 'end' must be a whole number of cycles after 'start' 'end' value not changed 'family' argument seems not to be a valid family object 'family' not recognized 'file' must be a character string or connection 'filter' is longer than time series 'formula' has '.' in both left and right hand sides 'formula' missing 'formula' missing or incorrect 'formula' missing or invalid 'formula' must have both left and right hand sides 'formula' should be of the form response ~ group 'frequency' and 'deltat' are both not NULL and are inconsistent 'frequency' not changed 'height' must be at least %g, the maximal height of its components 'hybrid' is ignored for a 2 x 2 table 'id.n' must be in {1,..,%d} 'idvar' must uniquely identify records 'init' is of the wrong length 'init' must have %d column 'init' must have 1 or %d columns 'interval' must be a vector of length 2 'iter.max' must be positive 'k' and 'h' must be a scalar 'k' is bigger than 'n'! Changing 'k' to %d 'k' is not a kernel 'k' is not an integer 'k' must be in {1, 2, .. n - 1} 'k' must be odd! Changing 'k' to %d 'k' must be positive 'kernapply' is not available for object 'x' 'lag.max' must be at least 0 'lag.max' must be at least 1 'lengths(varying)' must all match 'length(times)' 'logLik.lm' does not support multiple responses 'low' and 'high' cannot be both TRUE 'lower' and 'upper' must be finite values 'm' is less than 1 'm' must be numeric with non-negative integers 'main' must be TRUE, FALSE, NULL or character (vector). 'margin' must contain names or numbers corresponding to 'table' 'margins' must be a numeric vector of length 2 'mlm' objects with weights are not supported 'model$order' must be of length 3 'model' must be list 'model.tables' is not implemented for multiple responses 'mu' must be a single number 'mult' must be integer >= 2, typically = 30 'n' must be a positive integer >= 'x' 'n' must be strictly positive 'n.ahead' must be at least 1 'newdata' does not have named columns matching one or more of the original columns 'newdata' does not have the correct number of columns 'newdata' had %d row 'newdata' had %d rows 'newdata' must be a matrix or data frame 'nknots' must be at least 1 'nknots' must be numeric (in {1,..,n}) 'nterms' is missing with no default 'object' does not include an error 'qr' component 'object' has no 'effects' component 'offset' must be numeric 'or' must be a single number between 0 and Inf 'order' must be a non-negative numeric vector of length 3 'order.dendrogram' requires a dendrogram 'order.max' must be < 'n.obs' 'order.max' must be < 'n.used' 'order.max' must be >= 0 'order.max' must be >= 1 'p' must be a single number between 0 and 1 'p' must be between 0 and 0.5 'p' must have the same length as 'x' and 'n' 'params' has wrong length 'plot.stepfun' called with wrong type of argument 'x' 'ppr' applies only to numerical variables 'princomp' can only be used with more units than variables 'print.level' must be in {0,1,2} 'prob' and 'mu' both specified 'probs' outside [0,1] 'proj' is not implemented for multiple responses 'r' is less than 0 'r' is less than 1 'r' must be a single positive number 'ratio' must be a single positive number 'ref' must be an existing level 'ref' must be of length one 'relevel' only for (unordered) factors 'relevel' only for unordered factors 'reorder.dendrogram' requires a dendrogram 'row.var.names' missing 'sampleT' and 'nser' must be integer 'scale' is outside [0, 1] 'scores' argument is of the wrong length 'scores' must all be different numbers 'se.fit' not yet implemented for multivariate models 'seasonal$order' must be a non-negative numeric vector of length 3 'seasonal' must be a list with component 'order' 'span' must be between 0 and 1. 'spar' must be of length 1 'spline' requires n >= 1 'start' > 'end' 'start' and 'table' must be same length 'start' cannot be after 'end' 'start' must have %d row 'start' must have %d rows 'start' value not changed 'start.innov' is too short: need %d point 'start.innov' is too short: need %d points 'termlabels' must be a character vector of length at least one 'termobj' must be a object of class %s 'ties' is not "ordered", a function, or list(<string>, <function>) 'tol' must be strictly positive and finite 'trace != 0' needs 'REPORT >= 1' 'ts' object must have one or more observations 'type' must be 1 or 3 for ordered factors 'variance' "%s" is invalid: possible values are "mu(1-mu)", "mu", "mu^2", "mu^3" and "constant" 'varying' arguments must be the same length 'varying' has wrong length 'varying' must be in seq_along(pars) 'varying' must be logical, integer or character 'varying' must be nonempty list or vector 'weights' as formula should be one-sided 'weights' must all be finite 'weights' must be a numeric vector 'weights' must not be negative 'which' must be in 1:6 'which' specified no factors 'which' specified some non-factors which will be dropped 'x' and 'T' have incompatible length 'x' and 'g' must have the same length 'x' and 'n' must have the same length 'x' and 'p' must have the same number of elements 'x' and 'w' must have the same length 'x' and 'weights' have unequal length 'x' and 'y' must have at least 2 levels 'x' and 'y' must have the same length 'x' and 'y' must have the same number of levels (minimum 2) 'x' contains missing values 'x' has been rounded to integer: %s 'x' has entries too large to be integer 'x' has rank 0 'x' is a list, so ignoring argument 'g' 'x' is empty 'x' is not a kernel 'x' is not a vector 'x' is not a vector or matrix 'x' is shorter than kernel 'k' 'x' must at least have 2 elements 'x' must be *strictly* increasing (non - NA) 'x' must be a 3-dimensional array 'x' must be a list with at least 2 elements 'x' must be a matrix 'x' must be a matrix or a data frame 'x' must be a numeric matrix 'x' must be a numeric vector 'x' must be a time series or an ar() fit 'x' must be an "ftable" object 'x' must be between %s and %s 'x' must be between -1 and 1 'x' must be between 0 and 1 for periodic smooth 'x' must be coefficient matrix/data frame 'x' must be finite, nonnegative, and integer 'x' must be non-negative 'x' must be nonnegative and integer 'x' must be numeric 'x' must be square with at least two rows and columns 'x' must contain finite values only 'x' must have 1 or more non-missing values 'x' must have 2 columns 'x' must have at least 2 rows and 2 columns 'x' must have at least 2 rows and columns 'x' must have length >= 1 'x', 'y', and 'z' must have the same length 'xi' does not have the right length 'xreg' and 'newxreg' have different numbers of columns 'xreg' is collinear 'xy.labels' must be logical or character 'y' has rank 0 'y' is missing for paired test 'y' must be a numeric vector 'y' must be increasing or decreasing 'y' must be numeric 'y' must be numeric or a function or a string naming a valid function 'y' must be numeric vector 'y' must be one longer than 'x' 'y', 'groups' and 'blocks' must have the same length (unordered) factors are not allowed , 0 (non-NA) cases 0s in V(mu) AIC is -infinity for this model, so 'step' cannot proceed AIC is not defined for this model, so 'step' cannot proceed ANOVA F-tests on an essentially perfect fit are unreliable Assuming constant prediction variance even though model fit is weighted Barrier algorithm ran out of iterations and did not converge Cannot compute exact p-value with ties Chi-squared approximation may be incorrect Colv = "Rowv" but nrow(x) != ncol(x) Consider providing an as.hclust.%s() method Design is unbalanced - use se.contrast() for se's Error() model is singular F test assumes 'quasi%s' family F test assumes quasi%s family MA part of model is not invertible MLE only implemented for univariate series NA lev[]; probably smoothing parameter 'spar' way too large! NA values not allowed in 'd' NA's are not allowed in 'groups' or 'blocks' NAs are not allowed NAs in '%s' NAs in 'x' NAs in 'x' must be the same row-wise NAs in V(mu) NAs in d(mu)/d(eta) NAs present: setting 'delta' to -1 No p1 in [0, p2] can be found to achieve the desired power No p2 in [p1, 1] can be found to achieve the desired power No significance level [0, 1] can be found to achieve the desired power Numeric 'all.knots' must be strictly increasing Objective function decreased at outer iteration %d Objective function increased at outer iteration %d PCA applies only to numerical variables Page [%d,%d]: i =%s; j =%s Parameter(s) Parameter: Quick-TRANSfer stage steps exceeded maximum (= %d) Refitting model to allow projection Reprofiling for Requested conf.level not achievable SEs for type '%s' are not yet implemented Selecting bandwidth *not* using 'weights' Standard error information not returned as design is unbalanced.
Standard errors can be obtained through 'se.contrast'. The "ward" method has been renamed to "ward.D"; note new "ward.D2" Unparseable 'response' "%s"; use is deprecated. Use as.name(.) or `..`! Waiting for profiling to be done... X does not define a subspace of M X matrix has rank %d, but only %d observation X matrix has rank %d, but only %d observations _NOT_ converged in %d iteration _NOT_ converged in %d iterations a limit is NA or NaN a two-sided formula is required all 'x' values are identical all 'x' values must be non-negative to fit the Weibull growth model all elements of 'which' must be between 1 and %d all entries of 'x' must be nonnegative and finite all groups must contain data all observations are in the same group all parameters were fixed all times contain an NA all weights should be non-negative alternative must be "two.sided", "less" or "greater" ambiguous clustering method ambiguous distance method anova is only defined for sequences of "nls" objects argument 'na.action' will be ignored argument 'object' has an impossible length argument 'p' must be numeric argument 'q' must be numeric argument 'sides' must be 1 or 2 argument 'sizes' must be a vector of length 2 argument 'subset' will be ignored argument 'x' cannot be coerced to class %s argument 'x' is missing -- it has been renamed from 'formula' argument 'x' must be a formula argument 'x' must be numeric argument does not include a 'qr' component argument does not include an 'effects' component arguments 'r' and 'c' must have the same sums arguments must have same length arguments must have the same length assuming prediction variance inversely proportional to weights used for fitting at least one entry of 'x' must be positive attempt to smooth NA values attempt to smooth non-numeric values attempting model selection on an essentially perfect fit is nonsense bad value for 'end' bad value for 'lag' or 'differences' bad value for 'start' bandwidth 'k' must be >= 1 and odd! baseline group number out of range biplots are not defined for complex PCA both 'span' and 'enp.target' specified: 'span' will be used both 'x' and 'covmat' were supplied: 'x' will be ignored both 'x' and 'y' must be non-empty bounds can only be used with method L-BFGS-B (or Brent) burn-in 'n.start' must be as long as 'ar + ma' but %d variable but %d variables but variable found had %d row but variables found have %d rows calling anova.lm(<fake-lm-object>) ... calling predict.lm(<fake-lm-object>) ... calling summary.lm(<fake-lm-object>) ... can use ci.type="ma" only if first lag is 0 cannot calculate REML log-likelihood for "nls" objects cannot change frequency from %g to %g cannot compute asymptotic confidence set or estimator cannot compute confidence interval when all observations are tied cannot compute confidence interval when all observations are zero or tied cannot compute confidence set, returning NA cannot compute estimate, returning NA cannot compute exact confidence interval with ties cannot compute exact confidence interval with zeroes cannot compute exact confidence intervals with ties cannot compute exact p-value with ties cannot compute exact p-value with zeroes cannot compute simulated p-value with zero marginals cannot create a formula from a zero-column data frame cannot find valid starting values: please specify some cannot fit an asymptotic regression model to these data cannot fit models without level ('alpha' must not be 0 or FALSE) cannot handle 'pairwise.complete.obs' cannot plot more than 10 series as "multiple" cannot recognize parameter name cannot rescale a constant/zero column to unit variance cannot simulate from non-integer prior.weights cannot use 'cor = TRUE' with a constant variable cannot use 'paired' in formula method cannot use dots in formula with given data cannot use more inner knots than unique 'x' values coefficients do not add to 1 collapsing to unique 'x' values column dendrogram ordering gave index of wrong length columns of 'contrast.obj' must define a contrast (sum to zero) columns of 'contrast.obj' must define a contrast(sum to zero) computation of exact probability failed, returning Monte Carlo approximation contrasts apply only to factors contrasts can be applied only to factors with 2 or more levels contrasts dropped from factor %s contrasts dropped from factor %s due to missing levels contrasts not defined for %d degrees of freedom convergence problem in zero finding: converting non-invertible initial MA values covariance matrix is not non-negative definite coverage probability out of range [0,1) cross-validation with non-unique 'x' values seems doubtful data are essentially constant data must be non-zero for multiplicative Holt-Winters dendrogram entries must be 1,2,..,%d (in any order), to be coercible to "hclust" dendrogram node with non-positive #{branches} dendrogram non-leaf node with non-positive #{branches} design is unbalanced so cannot proceed diag(V) had non-positive or NA entries; the non-finite result may be dubious did not converge in %d iteration did not converge in %d iterations did not succeed extending the interval endpoints for f(lower) * f(upper) <= 0 dimension 0 in 'x' or 'y' distances must be result of 'dist' or a square matrix down each dimension in table must be >= 2 each element of '%s' must be logical eff.aovlist: non-orthogonal contrasts would give an incorrect answer eig=TRUE is disregarded when list.=FALSE either 'k' or 'h' must be specified elements of 'k' must be between 1 and %d elements of 'n' must be positive elements of 'p' must be in (0,1) elements of 'x' must be nonnegative elements of 'x' must not be greater than those of 'n' empty cluster: try a better set of initial centers empty model supplied essentially perfect fit: summary may be unreliable exactly one of 'groups', 'n', 'between.var', 'within.var', 'power', and 'sig.level' must be NULL exactly one of 'n', 'delta', 'sd', 'power', and 'sig.level' must be NULL exactly one of 'n', 'p1', 'p2', 'power', and 'sig.level' must be NULL extending time series when replacing values extra argument %s is not of class "%s" extra arguments %s are not of class "%s" extra arguments discarded f() values at end points not of opposite sign f.lower = f(lower) is NA f.upper = f(upper) is NA factor %s has new level %s factor %s has new levels %s factor analysis applies only to numerical variables factor analysis requires at least three variables failed to guess time-varying variables from their names family '%s' not implemented first argument must be a "loess" object fitting parameter %s without any variables fitting parameters %s without any variables fitting to calculate the null deviance did not converge -- increase 'maxit'? for the '%s' family, y must be a vector of 0 and 1's
or a 2 column matrix where col 1 is no. successes and col 2 is no. failures formula '%s' must be of the form '~expr' formula 'x' must have both left and right hand sides formula missing frequency must be a positive integer >= 2 for BSM glm.fit: algorithm did not converge glm.fit: algorithm stopped at boundary value glm.fit: fitted probabilities numerically 0 or 1 occurred glm.fit: fitted rates numerically 0 occurred grouping factor must have exactly 2 levels hat values (leverages) are all = %s
and there are no factor predictors; no plot no. 5 if 'x' is not a matrix, 'y' must be given if 'x' is not an array, 'y' must be given if 'x' is not an array, 'z' must be given ignored ignoring prior weights incompatible dimensions inconsistent specification of 'ar' order inconsistent specification of 'ma' order incorrect dimensions for 'xi' incorrect length of 'x' incorrect specification for 'col.vars' incorrect specification for 'formula' incorrect specification for 'row.vars' incorrect variable names in lhs of formula incorrect variable names in rhs of formula increasing bw.SJ() search interval (%d) to [%.4g,%.4g] initial centers are not distinct initial value is not in the interior of the feasible region inner loop 1; cannot correct step size inner loop 2; cannot correct step size interactions are not allowed invalid 'attr(rhs, "gradient")' invalid 'SSinit' invalid 'abbr.colnames' invalid 'control' argument invalid 'control.spar' invalid 'data' argument invalid 'endrule' argument invalid 'extendInt'; please report invalid 'keep.stuff' invalid 'lm' object: no 'terms' component invalid 'method' argument invalid 'nb' invalid 'tree' ('merge' component) invalid 'use' argument invalid 'x' invalid 'y' invalid (length 0) node in dendrogram invalid NCOL(X) invalid NROW(X) invalid argument 'c' invalid argument 'cell' invalid argument 'degree' invalid argument 'lambda' invalid argument 'n' invalid argument 'omit' invalid argument 'r' invalid argument 'span' invalid argument to 'getProfile' invalid arguments invalid clustering method invalid dissimilarities invalid distance method invalid fitted means in empty model invalid formula invalid formula %s invalid formula %s: assignment is deprecated invalid formula %s: extraneous call to `%s` is deprecated invalid formula %s: not a call invalid formula in deriv invalid formula: %s invalid interpolation method invalid length of members invalid length(x) invalid linear predictor values in empty model invalid model QR matrix invalid ncol(x) invalid nrow(x) invalid number of points invalid parameter values invalid response type invalid value of %s iterTrace = %d is not obeyed since iterations = %d k must be between 2 and %d length mismatch in convolution length of 'center' must equal the number of columns in 'x' length of 'init' must equal length of 'filter' length of 'p' must be 1 or equal the number of columns of 'x' length of 'start' should equal %d and correspond to initial coefs for %s length of 'v.names' does not evenly divide length of 'varying' length of 'varying' must be the product of length of 'v.names' and length of 'times' length of 'wt' must equal the number of rows in 'x' length of FUN, %d,
does not match the length of the margins, %d length of choices must be 2 lengths of 'x' and 'w' must match lengths of 'x' and 'xreg' do not match levels truncated to positive values only link "%s" not available for %s family; available links are %s lm object does not have a proper 'qr' component.
Rank zero or should not have used lm(.., qr=FALSE). logical 'hessian' argument not allowed. See documentation. lower < upper is not fulfilled lower scope has term %s not included in model lower scope has terms %s not included in model lower-rank qr: determining non-estimable cases maximum number of iterations must be > 0 medpolish() did not converge in %d iteration medpolish() did not converge in %d iterations method = "Brent" is only available for one-dimensional optimization method = '%s' is not supported. Using 'qr' method L-BFGS-B uses 'factr' (and 'pgtol') instead of 'reltol' and 'abstol' midcache() of non-binary dendrograms only partly implemented minimum occurred at one end of the range mismatched 'x' and 'y' missing observations deleted missing or infinite values in inputs are not allowed missing or negative weights not allowed missing values and NaN's not allowed if 'na.rm' is FALSE missing values are not allowed in 'poly' missing values in 'filter' missing values in object missing values not allowed model frame and formula mismatch in model.matrix() models are not all fitted to the same number of observations models were not all fitted to the same size of dataset models with response %s removed because response differs from model 1 more cluster centers than data points more cluster centers than distinct data points. multivariate case with missing coefficients is not yet implemented must have 2 'symbols' for logical 'x' argument must have at least 4 observations to fit an 'SSfol' model must have length of response = length of second argument to 'SSfol' must have n >= 2 objects to cluster must have same number of columns in 'x' and 'centers' must not specify both 'spar' and 'lambda' must specify 'spans' or a valid kernel must supply 'formula' or 'data' must supply one or more vectors na.action must be a function names(hybridPars) should be NULL or be identical to the default's need 2 or more non-zero column marginals need 2 or more non-zero row marginals need CRAN package 'SuppDists' for simulation from the 'inverse.gaussian' family need an object with call component need at least 2 data points need at least 2 periods to compute seasonal start values need at least 2 points to select a bandwidth automatically need at least four unique 'x' values need at least two non-NA values to interpolate need dendrograms where all leaves have labels need multiple responses need numeric data need result of smooth.spline(keep.data = TRUE) negative values not allowed for the 'Poisson' family negative values not allowed for the 'quasiPoisson' family negative weights not allowed neither 'x' nor 'covmat' supplied no "nobs" attribute is available no 'add1' method implemented for "mlm" models no 'as.stepfun' method available for 'x' no 'drop1' method for "mlm" models no 'getInitial' method found for "%s" objects no 'nobs' method is available no 'reshapeWide' attribute, must specify 'varying' no degrees of freedom for residuals no factors in the fitted model no finite observations no models to compare no observations informative at iteration %d no parameters to fit no replacement values supplied no rows to aggregate no scores are available: refit with 'retx=TRUE' no sign change found in %d iterations no solution in the specified range of bandwidths no starting values specified no starting values supplied no terms component nor attribute no terms in scope no terms in scope for adding to object no time series supplied no valid set of coefficients has been found: please supply starting values non-NA residual length does not match cases used in fitting non-factors ignored: %s non-finite 'bw' non-finite 'from' non-finite 'to' non-finite coefficients at iteration %d non-integer #successes in a %s glm! non-integer counts in a %s glm! non-intersecting series non-leaf subtree of length 0 non-list contrasts argument ignored non-positive values not allowed for the 'Gamma' family non-square matrix non-stationary AR part non-stationary AR part from CSS non-stationary seasonal AR part non-stationary seasonal AR part from CSS non-time series not of the correct length not a valid "smooth.spline" object not a valid step function not all series have the same frequency not all series have the same phase not an unreplicated complete block design not enough 'x' data not enough 'x' observations not enough 'y' data not enough 'y' observations not enough (non-missing) 'x' observations not enough data not enough degrees of freedom to define contrasts not enough finite observations not enough groups not enough observations not plotting observations with leverage one:
%s not using invalid df; must have 1 < df <= n := #{unique x} = nothing to tabulate number of 'cutpoints' must be one less than number of symbols number of 'cutpoints' must be one more than number of symbols number of cluster centres must lie between 1 and nrow(x) number of differences must be a positive integer number of groups must be at least 2 number of observations in 'x' and 'y' must match. number of observations in each group must be at least 2 number of offsets is %d should equal %d (number of observations) number of offsets is %d, should equal %d (number of observations) number of rows in use has changed: remove missing values? number of series in 'object' and 'newdata' do not match number of values supplied is not a sub-multiple of the number of values to be replaced number of weights = %d should equal %d (number of responses) number of weights must match number of observations. numeric 'all.knots' must cover [0,1] (= the transformed data-range) numeric contrasts or contrast name expected numeric y must be supplied.
For density estimation use density() object '%s' has no scores object must be of class %s or %s object not interpretable as a factor observations with 0 weight not used in calculating standard deviation observations with 0 weights not used observations with zero weight not used for calculating dispersion one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly only %d case only %d cases only %d of the first %d eigenvalues are > 0 only 1-4 predictors are allowed only implemented for univariate time series only replacement of elements is allowed only univariate series are allowed optimization difficulties: %s optimization failure option "show.coef.Pvalues" is invalid: assuming TRUE option "show.signif.stars" is invalid: assuming TRUE orthogonal polynomials cannot be represented accurately enough for %d degrees of freedom p-value will be approximate in the presence of ties parameter %s does not occur in the model formula parameters %s do not occur in the model formula parameters without starting value in 'data': %s pooling of SD is incompatible with paired tests positive values only are allowed for the 'inverse.gaussian' family possible convergence problem: 'optim' gave code = %d and message %s possible convergence problem: optim gave code = %d prediction from rank-deficient fit predictions on current data refer to _future_ responses predictors must all be numeric probabilities must be finite, non-negative and not all 0 probabilities must be non-negative. probabilities must sum to 1. profiling has found a better solution, so original fit had not converged read the documentation for 'trace' more carefully ref = %d must be in 1L:%d requested conf.level not achievable requested scores without an 'x' matrix residual degrees of freedom in object suggest this is not an "lm" fit residuals have rank %d < %d residuals have rank %s < %s response not allowed in formula right-hand side of formula is not a call row dendrogram ordering gave index of wrong length sample size in each stratum must be > 1 sample size must be between 3 and 5000 samples differ in location: cannot compute confidence set, returning NA scatter plots only for univariate time series scope is not a subset of term labels seasonal MA part of model is not invertible series is corrupt, with no 'tsp' attribute series is corrupt: length %d with 'tsp' implying %d series is not periodic or has less than two periods setVarying : 'vary' length must match length of parameters setting '%s' in terms.formula() is deprecated setting df = 1 __use with care!__ simulate() is not yet implemented for multivariate lm() singular contrast matrix singular fit encountered singular gradient matrix at initial parameter estimates singularities in regression size != sum(x), i.e. one is wrong size cannot be NA nor exceed 65536 some AR parameters were fixed: setting transform.pars = FALSE some ARMA parameters were fixed: setting transform.pars = FALSE some constant variables (%s) are really varying some elements of 'x' are not numeric and will be coerced to numeric some terms will have NAs due to the limits of the method some weights should be positive specified parametric for all predictors specified the square of a factor predictor to be dropped when degree = 1 specified the square of a predictor to be dropped with only one numeric predictor specify 'rate' or 'scale' but not both specify exactly one of 'k' and 'h' specify exactly one of 'which' and 'x' spline: first and last y values differ - using y[1L] for both spline: first and last y values differ - using y[1] for both statistic. Waiting... step size truncated due to divergence step size truncated: out of bounds stepfun: 'x' must be ordered increasingly sum(weights) != 1 -- will not get true density supply both 'x' and 'y' or a matrix-like 'x' table 'x' should have 2 entries the 'height' component of 'tree' is not sorted (increasingly) the 'se.fit' argument is not yet implemented for "mlm" objects the 'split' argument must be a list the case k > 2 is unimplemented the contrast defined is empty (has no TRUE elements) the first value of the time series must not be missing the following arguments to 'anova.glm' are invalid and dropped: the series is entirely NA there are %d Error terms: only 1 is allowed there are %d Error terms: only 1 is allowed there are records with missing times, which will be dropped. there must be at least 2 observations in each group this fit does not inherit from "lm" ties should not be present for the one-sample Kolmogorov-Smirnov test time series contains internal NAs time series has no or less than 2 periods times to be replaced do not match too few cases i with h_ii > 0), n < k too few distinct input values to fit a Michaelis-Menten model too few distinct input values to fit a biexponential too few distinct input values to fit a four-parameter logistic too few distinct input values to fit a logistic model too few distinct input values to fit an asymptotic regression model too few distinct input values to fit the 'asympOff' model too few distinct input values to fit the 'asympOrig' model too few distinct input values to fit the Gompertz model too few distinct input values to fit the Weibull growth model too few groups too few non-missing observations too few observations to fit an asymptotic regression model too many replacement values supplied transformed ARMA parameters were fixed type '%s' is not implemented yet type = "partial" is not yet implemented unable to optimize from this starting value unable to optimize from these starting values unequal number of rows in 'cancor' univariate time series only unknown bandwidth rule unknown name %s in the 'split' list unknown names %s in the 'split' list unknown named kernel unknown names in control: unknown string value for s.window unrecognized control element named %s ignored unrecognized control elements named %s ignored up upper and lower bounds ignored unless algorithm = "port" upper scope has term %s not included in model upper scope has terms %s not included in model use only with "lm" objects using F test with a '%s' family is inappropriate using F test with a fixed dispersion is inappropriate using the %d/%d row from a combined fit using the %d/%d rows from a combined fit using type = "numeric" with a factor response will be ignored using weights as inverse variances using weights as shape parameters value of 'epsilon' must be > 0 variable '%s' is absent, its contrast will be ignored variable '%s' is not a factor variable '%s' was fitted with type "%s" but type "%s" was supplied variables %s were specified with different types from the fit weights are not supported in a multistratum aov() fit weights must be non-negative and not all zero wrong embedding dimension wrong k / cs.ind wrong length for 'fixed' wrong method wrong number of columns in 'x' wrong number of columns in new data: wrong number of contrast matrix rows x is not a vector or univariate time series x$lag must have at least 1 column x.ret=TRUE is disregarded when list.=FALSE x[1] != r[1]; using x[1] for diagonal x[] and prob[] must be equal length vectors. y is empty or has only NAs y values must be 0 <= y <= 1 zero non-NA points zero-variance series Project-Id-Version: R 4.4.0
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%d factor is too many for %d variables %d factors are too many for %d variables %d missing value deleted %d missing values deleted %d observation deleted due to missingness %d observations deleted due to missingness %d observation with NA, NaN or Inf deleted %d observations with NAs, NaNs and/or Infs deleted %s applies only to two-way tables %s link not recognised %s must be 0 or 1 %s needs package ‘Matrix’ correctly installed %s: NAs produced for non-estimable cases %s; attr(*, "non-estim") has doubtful cases %s; consider predict(., rankdeficient="NA") ‘%s’ cannot be of mode ‘%s’ ‘%s’ must be a character string ‘%s’ must be numeric in [0, 1] ‘A’ must be an array or table ‘ColSideColors’ must be a character vector of length ncol(x) ‘P.values’ is TRUE, but ‘has.Pvalue’ is not ‘RowSideColors’ must be a character vector of length nrow(x) ‘T’ must be nonnegative ‘V’ is not a square numeric matrix ‘X’ is not a dendrogram ‘X’ matrix has %d case (row) ‘X’ matrix has %d cases (rows) ‘X’ matrix was collinear ‘Y’ has %d case (row) ‘Y’ has %d cases (rows) ‘acf’ must be of length two or more ‘all.knots’ is TRUE; ‘nknots’ specification is disregarded ‘all.knots’ is vector of knots; ‘nknots’ specification is disregarded ‘alpha’, ‘beta’ and ‘gamma’ must be within the unit interval ‘anova’ is only defined for sequences of "nls" objects ‘anova’ object must have colnames ‘approx’ requires n >= 1 ‘ar’ part of model is not stationary ‘bw’ is not positive. ‘by’ must be a list ‘centers’ must be a number or a matrix ‘circular’ must be logical and not NA ‘coef’ does not have the correct length ‘coef’ must be a vector ‘coef’ must define a contrast, i.e., sum to 0 ‘coef’ must have same length as ‘contrast.obj’ ‘col.vars’ missing or incorrect ‘conf.level’ is not a probability ‘conf.level’ must be a single number between 0 and 1 ‘contrasts.arg’ argument must be named ‘control’ argument must be a named list ‘covmat’ is not a valid covariance list ‘covmat’ is of unknown type ‘cterms’ argument must match terms in model object ‘cutpoints’ must be unique in 0 < cuts < 1, but are = ‘cutpoints’ must be unique, but are = ‘cv’ must not be NA when ‘df’ is specified ‘data’ must be a data.frame, environment, or list ‘data’ must be a data.frame, not a matrix or an array ‘data’ must be a list or an environment ‘degree’ must be 0, 1 or 2 ‘degree’ must be at least 1 ‘degree’ must be less than number of unique points ‘deriv’ must be between 0 and 3 ‘end’ must be a whole number of cycles after ‘start’ ‘end’ value not changed ‘family’ argument seems not to be a valid family object ‘family’ not recognized ‘file’ must be a character string or connection ‘filter’ is longer than time series ‘formula’ has ‘.’ in both left and right hand sides ‘formula’ missing ‘formula’ missing or incorrect ‘formula’ missing or invalid ‘formula’ must have both left and right hand sides ‘formula’ should be of the form response ~ group ‘frequency’ and ‘deltat’ are both not NULL and are inconsistent ‘frequency’ not changed ‘height’ must be at least %g, the maximal height of its components ‘hybrid’ is ignored for a 2 x 2 table ‘id.n’ must be in {1,..,%d} ‘idvar’ must uniquely identify records ‘init’ is of the wrong length ‘init’ must have %d column ‘init’ must have 1 or %d columns ‘interval’ must be a vector of length 2 ‘iter.max’ must be positive ‘k’ and ‘h’ must be a scalar ‘k’ is bigger than ‘n’! Changing ‘k’ to %d ‘k’ is not a kernel ‘k’ is not an integer ‘k’ must be in {1, 2, .. n - 1} ‘k’ must be odd! Changing ‘k’ to %d ‘k’ must be positive ‘kernapply’ is not available for object ‘x’ ‘lag.max’ must be at least 0 ‘lag.max’ must be at least 1 ‘lengths(varying)’ must all match ‘length(times)’ ‘logLik.lm’ does not support multiple responses ‘low’ and ‘high’ cannot be both TRUE ‘lower’ and ‘upper’ must be finite values ‘m’ is less than 1 ‘m’ must be numeric with non-negative integers ‘main’ must be TRUE, FALSE, NULL or character (vector). ‘margin’ must contain names or numbers corresponding to ‘table’ ‘margins’ must be a numeric vector of length 2 ‘mlm’ objects with weights are not supported ‘model$order’ must be of length 3 ‘model’ must be list ‘model.tables’ is not implemented for multiple responses ‘mu’ must be a single number ‘mult’ must be integer >= 2, typically = 30 ‘n’ must be a positive integer >= ‘x’ ‘n’ must be strictly positive ‘n.ahead’ must be at least 1 ‘newdata’ does not have named columns matching one or more of the original columns ‘newdata’ does not have the correct number of columns ‘newdata’ had %d row ‘newdata’ had %d rows ‘newdata’ must be a matrix or data frame ‘nknots’ must be at least 1 ‘nknots’ must be numeric (in {1,..,n}) ‘nterms’ is missing with no default ‘object’ does not include an error ‘qr’ component ‘object’ has no ‘effects’ component ‘offset’ must be numeric ‘or’ must be a single number between 0 and Inf ‘order’ must be a non-negative numeric vector of length 3 ‘order.dendrogram’ requires a dendrogram ‘order.max’ must be < ‘n.obs’ ‘order.max’ must be < ‘n.used’ ‘order.max’ must be >= 0 ‘order.max’ must be >= 1 ‘p’ must be a single number between 0 and 1 ‘p’ must be between 0 and 0.5 ‘p’ must have the same length as ‘x’ and ‘n’ ‘params’ has wrong length ‘plot.stepfun’ called with wrong type of argument ‘x’ ‘ppr’ applies only to numerical variables ‘princomp’ can only be used with more units than variables ‘print.level’ must be in {0,1,2} ‘prob’ and ‘mu’ both specified ‘probs’ outside [0,1] ‘proj’ is not implemented for multiple responses ‘r’ is less than 0 ‘r’ is less than 1 ‘r’ must be a single positive number ‘ratio’ must be a single positive number ‘ref’ must be an existing level ‘ref’ must be of length one ‘relevel’ only for (unordered) factors ‘relevel’ only for unordered factors ‘reorder.dendrogram’ requires a dendrogram ‘row.var.names’ missing ‘sampleT’ and ‘nser’ must be integer ‘scale’ is outside [0, 1] ‘scores’ argument is of the wrong length ‘scores’ must all be different numbers ‘se.fit’ not yet implemented for multivariate models ‘seasonal$order’ must be a non-negative numeric vector of length 3 ‘seasonal’ must be a list with component ‘order’ ‘span’ must be between 0 and 1. ‘spar’ must be of length 1 ‘spline’ requires n >= 1 ‘start’ > ‘end’ ‘start’ and ‘table’ must be same length ‘start’ cannot be after ‘end’ ‘start’ must have %d row ‘start’ must have %d rows ‘start’ value not changed ‘start.innov’ is too short: need %d point ‘start.innov’ is too short: need %d points ‘termlabels’ must be a character vector of length at least one ‘termobj’ must be a object of class %s ‘ties’ is not "ordered", a function, or list(<string>, <function>) ‘tol’ must be strictly positive and finite ‘trace != 0’ needs ‘REPORT >= 1’ ‘ts’ object must have one or more observations ‘type’ must be 1 or 3 for ordered factors ‘variance’ "%s" is invalid: possible values are "mu(1-mu)", "mu", "mu^2", "mu^3" and "constant" ‘varying’ arguments must be the same length ‘varying’ has wrong length ‘varying’ must be in seq_along(pars) ‘varying’ must be logical, integer or character ‘varying’ must be nonempty list or vector ‘weights’ as formula should be one-sided ‘weights’ must all be finite ‘weights’ must be a numeric vector ‘weights’ must not be negative ‘which’ must be in 1:6 ‘which’ specified no factors ‘which’ specified some non-factors which will be dropped ‘x’ and ‘T’ have incompatible length ‘x’ and ‘g’ must have the same length ‘x’ and ‘n’ must have the same length ‘x’ and ‘p’ must have the same number of elements ‘x’ and ‘w’ must have the same length ‘x’ and ‘weights’ have unequal length ‘x’ and ‘y’ must have at least 2 levels ‘x’ and ‘y’ must have the same length ‘x’ and ‘y’ must have the same number of levels (minimum 2) ‘x’ contains missing values ‘x’ has been rounded to integer: %s ‘x’ has entries too large to be integer ‘x’ has rank 0 ‘x’ is a list, so ignoring argument ‘g’ ‘x’ is empty ‘x’ is not a kernel ‘x’ is not a vector ‘x’ is not a vector or matrix ‘x’ is shorter than kernel ‘k’ ‘x’ must at least have 2 elements ‘x’ must be *strictly* increasing (non - NA) ‘x’ must be a 3-dimensional array ‘x’ must be a list with at least 2 elements ‘x’ must be a matrix ‘x’ must be a matrix or a data frame ‘x’ must be a numeric matrix ‘x’ must be a numeric vector ‘x’ must be a time series or an ar() fit ‘x’ must be an "ftable" object ‘x’ must be between %s and %s ‘x’ must be between -1 and 1 ‘x’ must be between 0 and 1 for periodic smooth ‘x’ must be coefficient matrix/data frame ‘x’ must be finite, nonnegative, and integer ‘x’ must be non-negative ‘x’ must be nonnegative and integer ‘x’ must be numeric ‘x’ must be square with at least two rows and columns ‘x’ must contain finite values only ‘x’ must have 1 or more non-missing values ‘x’ must have 2 columns ‘x’ must have at least 2 rows and 2 columns ‘x’ must have at least 2 rows and columns ‘x’ must have length >= 1 ‘x’, ‘y’, and ‘z’ must have the same length ‘xi’ does not have the right length ‘xreg’ and ‘newxreg’ have different numbers of columns ‘xreg’ is collinear ‘xy.labels’ must be logical or character ‘y’ has rank 0 ‘y’ is missing for paired test ‘y’ must be a numeric vector ‘y’ must be increasing or decreasing ‘y’ must be numeric ‘y’ must be numeric or a function or a string naming a valid function ‘y’ must be numeric vector ‘y’ must be one longer than ‘x’ ‘y’, ‘groups’ and ‘blocks’ must have the same length (unordered) factors are not allowed , 0 (non-NA) cases 0s in V(mu) AIC is -infinity for this model, so ‘step’ cannot proceed AIC is not defined for this model, so ‘step’ cannot proceed ANOVA F-tests on an essentially perfect fit are unreliable Assuming constant prediction variance even though model fit is weighted Barrier algorithm ran out of iterations and did not converge Cannot compute exact p-value with ties Chi-squared approximation may be incorrect Colv = "Rowv" but nrow(x) != ncol(x) Consider providing an as.hclust.%s() method Design is unbalanced - use se.contrast() for se's Error() model is singular F test assumes ‘quasi%s’ family F test assumes quasi%s family MA part of model is not invertible MLE only implemented for univariate series NA lev[]; probably smoothing parameter ‘spar’ way too large! NA values not allowed in ‘d’ NA's are not allowed in ‘groups’ or ‘blocks’ NAs are not allowed NAs in ‘%s’ NAs in ‘x’ NAs in ‘x’ must be the same row-wise NAs in V(mu) NAs in d(mu)/d(eta) NAs present: setting ‘delta’ to -1 No p1 in [0, p2] can be found to achieve the desired power No p2 in [p1, 1] can be found to achieve the desired power No significance level [0, 1] can be found to achieve the desired power Numeric ‘all.knots’ must be strictly increasing Objective function decreased at outer iteration %d Objective function increased at outer iteration %d PCA applies only to numerical variables Page [%d,%d]: i =%s; j =%s Parameter(s) Parameter: Quick-TRANSfer stage steps exceeded maximum (= %d) Refitting model to allow projection Reprofiling for Requested conf.level not achievable SEs for type ‘%s’ are not yet implemented Selecting bandwidth *not* using ‘weights’ Standard error information not returned as design is unbalanced.
Standard errors can be obtained through ‘se.contrast’. The "ward" method has been renamed to "ward.D"; note new "ward.D2" Unparseable ‘response’ "%s"; use is deprecated. Use as.name(.) or `..`! Waiting for profiling to be done... X does not define a subspace of M X matrix has rank %d, but only %d observation X matrix has rank %d, but only %d observations _NOT_ converged in %d iteration _NOT_ converged in %d iterations a limit is NA or NaN a two-sided formula is required all ‘x’ values are identical all ‘x’ values must be non-negative to fit the Weibull growth model all elements of ‘which’ must be between 1 and %d all entries of ‘x’ must be nonnegative and finite all groups must contain data all observations are in the same group all parameters were fixed all times contain an NA all weights should be non-negative alternative must be "two.sided", "less" or "greater" ambiguous clustering method ambiguous distance method anova is only defined for sequences of "nls" objects argument ‘na.action’ will be ignored argument ‘object’ has an impossible length argument ‘p’ must be numeric argument ‘q’ must be numeric argument ‘sides’ must be 1 or 2 argument ‘sizes’ must be a vector of length 2 argument ‘subset’ will be ignored argument ‘x’ cannot be coerced to class %s argument ‘x’ is missing -- it has been renamed from ‘formula’ argument ‘x’ must be a formula argument ‘x’ must be numeric argument does not include a ‘qr’ component argument does not include an ‘effects’ component arguments ‘r’ and ‘c’ must have the same sums arguments must have same length arguments must have the same length assuming prediction variance inversely proportional to weights used for fitting at least one entry of ‘x’ must be positive attempt to smooth NA values attempt to smooth non-numeric values attempting model selection on an essentially perfect fit is nonsense bad value for ‘end’ bad value for ‘lag’ or ‘differences’ bad value for ‘start’ bandwidth ‘k’ must be >= 1 and odd! baseline group number out of range biplots are not defined for complex PCA both ‘span’ and ‘enp.target’ specified: ‘span’ will be used both ‘x’ and ‘covmat’ were supplied: ‘x’ will be ignored both ‘x’ and ‘y’ must be non-empty bounds can only be used with method L-BFGS-B (or Brent) burn-in ‘n.start’ must be as long as ‘ar + ma’ but %d variable but %d variables but variable found had %d row but variables found have %d rows calling anova.lm(<fake-lm-object>) ... calling predict.lm(<fake-lm-object>) ... calling summary.lm(<fake-lm-object>) ... can use ci.type="ma" only if first lag is 0 cannot calculate REML log-likelihood for "nls" objects cannot change frequency from %g to %g cannot compute asymptotic confidence set or estimator cannot compute confidence interval when all observations are tied cannot compute confidence interval when all observations are zero or tied cannot compute confidence set, returning NA cannot compute estimate, returning NA cannot compute exact confidence interval with ties cannot compute exact confidence interval with zeroes cannot compute exact confidence intervals with ties cannot compute exact p-value with ties cannot compute exact p-value with zeroes cannot compute simulated p-value with zero marginals cannot create a formula from a zero-column data frame cannot find valid starting values: please specify some cannot fit an asymptotic regression model to these data cannot fit models without level (‘alpha’ must not be 0 or FALSE) cannot handle ‘pairwise.complete.obs’ cannot plot more than 10 series as "multiple" cannot recognize parameter name cannot rescale a constant/zero column to unit variance cannot simulate from non-integer prior.weights cannot use ‘cor = TRUE’ with a constant variable cannot use ‘paired’ in formula method cannot use dots in formula with given data cannot use more inner knots than unique ‘x’ values coefficients do not add to 1 collapsing to unique ‘x’ values column dendrogram ordering gave index of wrong length columns of ‘contrast.obj’ must define a contrast (sum to zero) columns of ‘contrast.obj’ must define a contrast(sum to zero) computation of exact probability failed, returning Monte Carlo approximation contrasts apply only to factors contrasts can be applied only to factors with 2 or more levels contrasts dropped from factor %s contrasts dropped from factor %s due to missing levels contrasts not defined for %d degrees of freedom convergence problem in zero finding: converting non-invertible initial MA values covariance matrix is not non-negative definite coverage probability out of range [0,1) cross-validation with non-unique ‘x’ values seems doubtful data are essentially constant data must be non-zero for multiplicative Holt-Winters dendrogram entries must be 1,2,..,%d (in any order), to be coercible to "hclust" dendrogram node with non-positive #{branches} dendrogram non-leaf node with non-positive #{branches} design is unbalanced so cannot proceed diag(V) had non-positive or NA entries; the non-finite result may be dubious did not converge in %d iteration did not converge in %d iterations did not succeed extending the interval endpoints for f(lower) * f(upper) <= 0 dimension 0 in ‘x’ or ‘y’ distances must be result of ‘dist’ or a square matrix down each dimension in table must be >= 2 each element of ‘%s’ must be logical eff.aovlist: non-orthogonal contrasts would give an incorrect answer eig=TRUE is disregarded when list.=FALSE either ‘k’ or ‘h’ must be specified elements of ‘k’ must be between 1 and %d elements of ‘n’ must be positive elements of ‘p’ must be in (0,1) elements of ‘x’ must be nonnegative elements of ‘x’ must not be greater than those of ‘n’ empty cluster: try a better set of initial centers empty model supplied essentially perfect fit: summary may be unreliable exactly one of ‘groups’, ‘n’, ‘between.var’, ‘within.var’, ‘power’, and ‘sig.level’ must be NULL exactly one of ‘n’, ‘delta’, ‘sd’, ‘power’, and ‘sig.level’ must be NULL exactly one of ‘n’, ‘p1’, ‘p2’, ‘power’, and ‘sig.level’ must be NULL extending time series when replacing values extra argument %s is not of class "%s" extra arguments %s are not of class "%s" extra arguments discarded f() values at end points not of opposite sign f.lower = f(lower) is NA f.upper = f(upper) is NA factor %s has new level %s factor %s has new levels %s factor analysis applies only to numerical variables factor analysis requires at least three variables failed to guess time-varying variables from their names family ‘%s’ not implemented first argument must be a "loess" object fitting parameter %s without any variables fitting parameters %s without any variables fitting to calculate the null deviance did not converge -- increase ‘maxit’? for the ‘%s’ family, y must be a vector of 0 and 1's
or a 2 column matrix where col 1 is no. successes and col 2 is no. failures formula ‘%s’ must be of the form ‘~expr’ formula ‘x’ must have both left and right hand sides formula missing frequency must be a positive integer >= 2 for BSM glm.fit: algorithm did not converge glm.fit: algorithm stopped at boundary value glm.fit: fitted probabilities numerically 0 or 1 occurred glm.fit: fitted rates numerically 0 occurred grouping factor must have exactly 2 levels hat values (leverages) are all = %s
and there are no factor predictors; no plot no. 5 if ‘x’ is not a matrix, ‘y’ must be given if ‘x’ is not an array, ‘y’ must be given if ‘x’ is not an array, ‘z’ must be given ignored ignoring prior weights incompatible dimensions inconsistent specification of ‘ar’ order inconsistent specification of ‘ma’ order incorrect dimensions for ‘xi’ incorrect length of ‘x’ incorrect specification for ‘col.vars’ incorrect specification for ‘formula’ incorrect specification for ‘row.vars’ incorrect variable names in lhs of formula incorrect variable names in rhs of formula increasing bw.SJ() search interval (%d) to [%.4g,%.4g] initial centers are not distinct initial value is not in the interior of the feasible region inner loop 1; cannot correct step size inner loop 2; cannot correct step size interactions are not allowed invalid ‘attr(rhs, "gradient")’ invalid ‘SSinit’ invalid ‘abbr.colnames’ invalid ‘control’ argument invalid ‘control.spar’ invalid ‘data’ argument invalid ‘endrule’ argument invalid ‘extendInt’; please report invalid ‘keep.stuff’ invalid ‘lm’ object: no ‘terms’ component invalid ‘method’ argument invalid ‘nb’ invalid ‘tree’ (‘merge’ component) invalid ‘use’ argument invalid ‘x’ invalid ‘y’ invalid (length 0) node in dendrogram invalid NCOL(X) invalid NROW(X) invalid argument ‘c’ invalid argument ‘cell’ invalid argument ‘degree’ invalid argument ‘lambda’ invalid argument ‘n’ invalid argument ‘omit’ invalid argument ‘r’ invalid argument ‘span’ invalid argument to ‘getProfile’ invalid arguments invalid clustering method invalid dissimilarities invalid distance method invalid fitted means in empty model invalid formula invalid formula %s invalid formula %s: assignment is deprecated invalid formula %s: extraneous call to `%s` is deprecated invalid formula %s: not a call invalid formula in deriv invalid formula: %s invalid interpolation method invalid length of members invalid length(x) invalid linear predictor values in empty model invalid model QR matrix invalid ncol(x) invalid nrow(x) invalid number of points invalid parameter values invalid response type invalid value of %s iterTrace = %d is not obeyed since iterations = %d k must be between 2 and %d length mismatch in convolution length of ‘center’ must equal the number of columns in ‘x’ length of ‘init’ must equal length of ‘filter’ length of ‘p’ must be 1 or equal the number of columns of ‘x’ length of ‘start’ should equal %d and correspond to initial coefs for %s length of ‘v.names’ does not evenly divide length of ‘varying’ length of ‘varying’ must be the product of length of ‘v.names’ and length of ‘times’ length of ‘wt’ must equal the number of rows in ‘x’ length of FUN, %d,
does not match the length of the margins, %d length of choices must be 2 lengths of ‘x’ and ‘w’ must match lengths of ‘x’ and ‘xreg’ do not match levels truncated to positive values only link "%s" not available for %s family; available links are %s lm object does not have a proper ‘qr’ component.
Rank zero or should not have used lm(.., qr=FALSE). logical ‘hessian’ argument not allowed. See documentation. lower < upper is not fulfilled lower scope has term %s not included in model lower scope has terms %s not included in model lower-rank qr: determining non-estimable cases maximum number of iterations must be > 0 medpolish() did not converge in %d iteration medpolish() did not converge in %d iterations method = "Brent" is only available for one-dimensional optimization method = ‘%s’ is not supported. Using ‘qr’ method L-BFGS-B uses ‘factr’ (and ‘pgtol’) instead of ‘reltol’ and ‘abstol’ midcache() of non-binary dendrograms only partly implemented minimum occurred at one end of the range mismatched ‘x’ and ‘y’ missing observations deleted missing or infinite values in inputs are not allowed missing or negative weights not allowed missing values and NaN's not allowed if ‘na.rm’ is FALSE missing values are not allowed in ‘poly’ missing values in ‘filter’ missing values in object missing values not allowed model frame and formula mismatch in model.matrix() models are not all fitted to the same number of observations models were not all fitted to the same size of dataset models with response %s removed because response differs from model 1 more cluster centers than data points more cluster centers than distinct data points. multivariate case with missing coefficients is not yet implemented must have 2 ‘symbols’ for logical ‘x’ argument must have at least 4 observations to fit an ‘SSfol’ model must have length of response = length of second argument to ‘SSfol’ must have n >= 2 objects to cluster must have same number of columns in ‘x’ and ‘centers’ must not specify both ‘spar’ and ‘lambda’ must specify ‘spans’ or a valid kernel must supply ‘formula’ or ‘data’ must supply one or more vectors na.action must be a function names(hybridPars) should be NULL or be identical to the default's need 2 or more non-zero column marginals need 2 or more non-zero row marginals need CRAN package ‘SuppDists’ for simulation from the ‘inverse.gaussian’ family need an object with call component need at least 2 data points need at least 2 periods to compute seasonal start values need at least 2 points to select a bandwidth automatically need at least four unique ‘x’ values need at least two non-NA values to interpolate need dendrograms where all leaves have labels need multiple responses need numeric data need result of smooth.spline(keep.data = TRUE) negative values not allowed for the ‘Poisson’ family negative values not allowed for the ‘quasiPoisson’ family negative weights not allowed neither ‘x’ nor ‘covmat’ supplied no "nobs" attribute is available no ‘add1’ method implemented for "mlm" models no ‘as.stepfun’ method available for ‘x’ no ‘drop1’ method for "mlm" models no ‘getInitial’ method found for "%s" objects no ‘nobs’ method is available no ‘reshapeWide’ attribute, must specify ‘varying’ no degrees of freedom for residuals no factors in the fitted model no finite observations no models to compare no observations informative at iteration %d no parameters to fit no replacement values supplied no rows to aggregate no scores are available: refit with ‘retx=TRUE’ no sign change found in %d iterations no solution in the specified range of bandwidths no starting values specified no starting values supplied no terms component nor attribute no terms in scope no terms in scope for adding to object no time series supplied no valid set of coefficients has been found: please supply starting values non-NA residual length does not match cases used in fitting non-factors ignored: %s non-finite ‘bw’ non-finite ‘from’ non-finite ‘to’ non-finite coefficients at iteration %d non-integer #successes in a %s glm! non-integer counts in a %s glm! non-intersecting series non-leaf subtree of length 0 non-list contrasts argument ignored non-positive values not allowed for the ‘Gamma’ family non-square matrix non-stationary AR part non-stationary AR part from CSS non-stationary seasonal AR part non-stationary seasonal AR part from CSS non-time series not of the correct length not a valid "smooth.spline" object not a valid step function not all series have the same frequency not all series have the same phase not an unreplicated complete block design not enough ‘x’ data not enough ‘x’ observations not enough ‘y’ data not enough ‘y’ observations not enough (non-missing) ‘x’ observations not enough data not enough degrees of freedom to define contrasts not enough finite observations not enough groups not enough observations not plotting observations with leverage one:
%s not using invalid df; must have 1 < df <= n := #{unique x} = nothing to tabulate number of ‘cutpoints’ must be one less than number of symbols number of ‘cutpoints’ must be one more than number of symbols number of cluster centres must lie between 1 and nrow(x) number of differences must be a positive integer number of groups must be at least 2 number of observations in ‘x’ and ‘y’ must match. number of observations in each group must be at least 2 number of offsets is %d should equal %d (number of observations) number of offsets is %d, should equal %d (number of observations) number of rows in use has changed: remove missing values? number of series in ‘object’ and ‘newdata’ do not match number of values supplied is not a sub-multiple of the number of values to be replaced number of weights = %d should equal %d (number of responses) number of weights must match number of observations. numeric ‘all.knots’ must cover [0,1] (= the transformed data-range) numeric contrasts or contrast name expected numeric y must be supplied.
For density estimation use density() object ‘%s’ has no scores object must be of class %s or %s object not interpretable as a factor observations with 0 weight not used in calculating standard deviation observations with 0 weights not used observations with zero weight not used for calculating dispersion one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly only %d case only %d cases only %d of the first %d eigenvalues are > 0 only 1-4 predictors are allowed only implemented for univariate time series only replacement of elements is allowed only univariate series are allowed optimization difficulties: %s optimization failure option "show.coef.Pvalues" is invalid: assuming TRUE option "show.signif.stars" is invalid: assuming TRUE orthogonal polynomials cannot be represented accurately enough for %d degrees of freedom p-value will be approximate in the presence of ties parameter %s does not occur in the model formula parameters %s do not occur in the model formula parameters without starting value in ‘data’: %s pooling of SD is incompatible with paired tests positive values only are allowed for the ‘inverse.gaussian’ family possible convergence problem: ‘optim’ gave code = %d and message %s possible convergence problem: optim gave code = %d prediction from rank-deficient fit predictions on current data refer to _future_ responses predictors must all be numeric probabilities must be finite, non-negative and not all 0 probabilities must be non-negative. probabilities must sum to 1. profiling has found a better solution, so original fit had not converged read the documentation for ‘trace’ more carefully ref = %d must be in 1L:%d requested conf.level not achievable requested scores without an ‘x’ matrix residual degrees of freedom in object suggest this is not an "lm" fit residuals have rank %d < %d residuals have rank %s < %s response not allowed in formula right-hand side of formula is not a call row dendrogram ordering gave index of wrong length sample size in each stratum must be > 1 sample size must be between 3 and 5000 samples differ in location: cannot compute confidence set, returning NA scatter plots only for univariate time series scope is not a subset of term labels seasonal MA part of model is not invertible series is corrupt, with no ‘tsp’ attribute series is corrupt: length %d with ‘tsp’ implying %d series is not periodic or has less than two periods setVarying : ‘vary’ length must match length of parameters setting ‘%s’ in terms.formula() is deprecated setting df = 1 __use with care!__ simulate() is not yet implemented for multivariate lm() singular contrast matrix singular fit encountered singular gradient matrix at initial parameter estimates singularities in regression size != sum(x), i.e. one is wrong size cannot be NA nor exceed 65536 some AR parameters were fixed: setting transform.pars = FALSE some ARMA parameters were fixed: setting transform.pars = FALSE some constant variables (%s) are really varying some elements of ‘x’ are not numeric and will be coerced to numeric some terms will have NAs due to the limits of the method some weights should be positive specified parametric for all predictors specified the square of a factor predictor to be dropped when degree = 1 specified the square of a predictor to be dropped with only one numeric predictor specify ‘rate’ or ‘scale’ but not both specify exactly one of ‘k’ and ‘h’ specify exactly one of ‘which’ and ‘x’ spline: first and last y values differ - using y[1L] for both spline: first and last y values differ - using y[1] for both statistic. Waiting... step size truncated due to divergence step size truncated: out of bounds stepfun: ‘x’ must be ordered increasingly sum(weights) != 1 -- will not get true density supply both ‘x’ and ‘y’ or a matrix-like ‘x’ table ‘x’ should have 2 entries the ‘height’ component of ‘tree’ is not sorted (increasingly) the ‘se.fit’ argument is not yet implemented for "mlm" objects the ‘split’ argument must be a list the case k > 2 is unimplemented the contrast defined is empty (has no TRUE elements) the first value of the time series must not be missing the following arguments to ‘anova.glm’ are invalid and dropped: the series is entirely NA there are %d Error terms: only 1 is allowed there are %d Error terms: only 1 is allowed there are records with missing times, which will be dropped. there must be at least 2 observations in each group this fit does not inherit from "lm" ties should not be present for the one-sample Kolmogorov-Smirnov test time series contains internal NAs time series has no or less than 2 periods times to be replaced do not match too few cases i with h_ii > 0), n < k too few distinct input values to fit a Michaelis-Menten model too few distinct input values to fit a biexponential too few distinct input values to fit a four-parameter logistic too few distinct input values to fit a logistic model too few distinct input values to fit an asymptotic regression model too few distinct input values to fit the ‘asympOff’ model too few distinct input values to fit the ‘asympOrig’ model too few distinct input values to fit the Gompertz model too few distinct input values to fit the Weibull growth model too few groups too few non-missing observations too few observations to fit an asymptotic regression model too many replacement values supplied transformed ARMA parameters were fixed type ‘%s’ is not implemented yet type = "partial" is not yet implemented unable to optimize from this starting value unable to optimize from these starting values unequal number of rows in ‘cancor’ univariate time series only unknown bandwidth rule unknown name %s in the ‘split’ list unknown names %s in the ‘split’ list unknown named kernel unknown names in control: unknown string value for s.window unrecognized control element named %s ignored unrecognized control elements named %s ignored up upper and lower bounds ignored unless algorithm = "port" upper scope has term %s not included in model upper scope has terms %s not included in model use only with "lm" objects using F test with a ‘%s’ family is inappropriate using F test with a fixed dispersion is inappropriate using the %d/%d row from a combined fit using the %d/%d rows from a combined fit using type = "numeric" with a factor response will be ignored using weights as inverse variances using weights as shape parameters value of ‘epsilon’ must be > 0 variable ‘%s’ is absent, its contrast will be ignored variable ‘%s’ is not a factor variable ‘%s’ was fitted with type "%s" but type "%s" was supplied variables %s were specified with different types from the fit weights are not supported in a multistratum aov() fit weights must be non-negative and not all zero wrong embedding dimension wrong k / cs.ind wrong length for ‘fixed’ wrong method wrong number of columns in ‘x’ wrong number of columns in new data: wrong number of contrast matrix rows x is not a vector or univariate time series x$lag must have at least 1 column x.ret=TRUE is disregarded when list.=FALSE x[1] != r[1]; using x[1] for diagonal x[] and prob[] must be equal length vectors. y is empty or has only NAs y values must be 0 <= y <= 1 zero non-NA points zero-variance series